list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds behavioral context by specifying the exact fields returned (id, type, params, created_at, last_fired_at, fire_count) and clarifying it applies to the caller's subscriptions. It does not cover edge cases like pagination, but the annotations lower the burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the main action, and includes only essential information: scope, return fields, and usage. Every sentence earns its place without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema), the description is complete. It explains what is listed, what fields are returned, and when to use it, which is sufficient for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, include_inactive, is fully described in the schema with 'Include cancelled subscriptions in the response (default false).' The description adds no additional parameter semantics, so with 100% schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List the caller's active subscriptions', specifying the verb, resource, and scope. It also lists the returned fields, making it unambiguous. This distinguishes it from sibling tools like subscribe and unsubscribe by focusing on listing existing subscriptions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use the tool and references the subscription lifecycle, implicitly guiding away from subscribe/unsubscribe for review purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,596 tools, with beta currently identical to stable. The suite of six polymarket_* tools and the ai_visibility_check / scan_competitor_ai_presence pair also risk misselection, despite detailed descriptions.
The overwhelming majority of tools follow a clear snake_case verb_noun pattern (ask_pipeworx, resolve_entity, validate_claim, subscribe). Minor deviations exist for bare data endpoints like bank_rate, sonia, and eur_gbp, but these are still predictable and readable.
37 tools is heavy for a single server, beyond the 25-tool threshold indicating a bloated surface. While the broad research scope justifies some size, many tools are variants or wrappers of the same underlying router, which pads the count and creates cognitive load.
For a research and data gateway, the surface covers lookups, grounded answers, deep research, claim verification, entity resolution, prediction-market analysis, BoE series, memory, and subscriptions. Minor gaps exist—such as no direct update for stored entities and BoE series limited to four convenience wrappers—but agents can work around these.